Dialysis event prediction

Abstract

A method for training a predictive model includes training a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information. An augmented model is trained to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.

What is claimed is: 
     
         1 . A method for training a predictive model, comprising:
 training, using a hardware processor, a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information; and   training an augmented model to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.   
     
     
         2 . The method of  claim 1 , wherein the dynamic channel includes a series of long short-term memory layers. 
     
     
         3 . The method of  claim 1 , wherein the static channel includes a multi-layer perceptron. 
     
     
         4 . The method of  claim 1 , wherein training the dual-channel neural network model includes training a prediction multi-layer perceptron to determine the probability score, based on a static feature from the static channel and a temporal feature from the dynamic channel. 
     
     
         5 . The method of  claim 1 , wherein training the augmented model includes training gradient boosting trees to output feature importance scores for a static feature from the static channel and a temporal feature from the dynamic channel. 
     
     
         6 . The method of  claim 5 , wherein training the augmented model includes minimizing a loss function for a gradient boosting tree regressor: 
       
         
           
             
               l 
               = 
               
                 
                   1 
                   N 
                 
                 ⁢ 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                   ⁢ 
                   
                     
                        
                       
                         
                           
                             y 
                             ^ 
                           
                           i 
                         
                         - 
                         
                           
                             ( 
                             
                               
                                 y 
                                 ^ 
                               
                               new 
                             
                             ) 
                           
                           i 
                         
                       
                        
                     
                     2 
                     2 
                   
                 
               
             
           
         
       
       where N is a number of samples, ŷ i  is the probability score for the i th , (ŷ new ) i  is a newly obtained probability score for an i th  sample. 
     
     
         7 . The method of  claim 1 , wherein the temporal information includes time series measurements made of one or more characteristics of a patient. 
     
     
         8 . The method of  claim 7 , further comprising pre-processing the temporal information, including splitting time series measurements into windows of a predetermined length. 
     
     
         9 . The method of  claim 8 , wherein pre-processing the temporal information includes adding, as part of a first window, a measurement for a first characteristic that was made outside of the first window, responsive to a determination that the window includes no measurements for the first characteristic. 
     
     
         10 . The method of  claim 7 , wherein the temporal information includes blood test information, dialysis measurements, and past event incidences. 
     
     
         11 . A system for training a predictive model, comprising:
 a hardware processor; and   a memory that stores a computer program product, which, when executed by the hardware processor, causes the hardware processor to:
 train a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information; and 
 train an augmented model to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information. 
   
     
     
         12 . The system of  claim 11 , wherein the dynamic channel includes a series of long short-term memory layers. 
     
     
         13 . The system of  claim 11 , wherein the static channel includes a multi-layer perceptron. 
     
     
         14 . The system of  claim 11 , wherein the computer program product further causes the hardware processor to train a prediction multi-layer perceptron to determine the probability score, based on a static feature from the static channel and a temporal feature from the dynamic channel. 
     
     
         15 . The system of  claim 11 , wherein the computer program product further causes the hardware processor to train gradient boosting trees to output feature importance scores for a static feature from the static channel and a temporal feature from the dynamic channel. 
     
     
         16 . The system of  claim 15 , wherein the computer program product further causes the hardware processor to minimize a loss function for a gradient boosting tree regressor: 
       
         
           
             
               l 
               = 
               
                 
                   1 
                   N 
                 
                 ⁢ 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                   ⁢ 
                   
                     
                        
                       
                         
                           
                             y 
                             ^ 
                           
                           i 
                         
                         - 
                         
                           
                             ( 
                             
                               
                                 y 
                                 ^ 
                               
                               new 
                             
                             ) 
                           
                           i 
                         
                       
                        
                     
                     2 
                     2 
                   
                 
               
             
           
         
       
       where N is a number of samples, ŷ i  is the probability score for the i th , (y new ) i  is a newly obtained probability score for an i th  sample. 
     
     
         17 . The system of  claim 11 , wherein the temporal information includes time series measurements made of one or more characteristics of a patient. 
     
     
         18 . The system of  claim 17 , wherein the computer program product further causes the hardware processor to pre-process the temporal information, including splitting time series measurements into windows of a predetermined length. 
     
     
         19 . The system of  claim 18 , wherein the computer program product further causes the hardware processor to add, as part of a first window, a measurement for a first characteristic that was made outside of the first window, responsive to a determination that the window includes no measurements for the first characteristic. 
     
     
         20 . The system of  claim 17 , wherein the temporal information includes blood test information, dialysis measurements, and past event incidences.